Agent skill

bio-single-cell-multimodal-integration

Analyze multi-modal single-cell data (CITE-seq, Multiome, spatial). Use when working with data that measures multiple modalities per cell like RNA + protein or RNA + ATAC. Use when analyzing CITE-seq, Multiome, or other multi-modal single-cell data.

View SKILL.md on GitHub Repository
Stars 2,009
Forks 275

Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-single-cell-multimodal-integration

SKILL.md

Version Compatibility

Reference examples tested with: numpy 1.26+, scanpy 1.10+

Before using code patterns, verify installed versions match. If versions differ:

  • Python: pip show <package> then help(module.function) to check signatures
  • R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Multimodal Integration

"Integrate RNA and protein data from my CITE-seq experiment" → Jointly analyze multiple modalities (RNA + protein, RNA + ATAC) measured in the same cells using weighted nearest neighbor or factor analysis.

  • R: Seurat::FindMultiModalNeighbors() for WNN integration
  • Python: muon for MuData handling, scanpy + anndata for multimodal objects

Analyze multi-modal single-cell data where multiple measurements are made per cell.

Common Modalities

Technology Modalities Package
CITE-seq RNA + surface proteins (ADT) Seurat
10X Multiome RNA + ATAC Seurat, Signac, ArchR
SHARE-seq RNA + ATAC Seurat, Signac
Spatial (Visium) RNA + spatial coordinates Seurat, Squidpy

CITE-seq Analysis (Seurat)

Load Data

r
library(Seurat)

# Read 10X data with antibody capture
data <- Read10X('filtered_feature_bc_matrix/')

# Separate RNA and ADT
rna_counts <- data$`Gene Expression`
adt_counts <- data$`Antibody Capture`

# Create Seurat object with both assays
obj <- CreateSeuratObject(counts = rna_counts, assay = 'RNA')
obj[['ADT']] <- CreateAssayObject(counts = adt_counts)

QC and Normalization

r
# RNA QC (standard)
obj <- PercentageFeatureSet(obj, pattern = '^MT-', col.name = 'percent.mt')
obj <- subset(obj, nFeature_RNA > 200 & percent.mt < 20)

# Normalize RNA
obj <- NormalizeData(obj, assay = 'RNA')
obj <- FindVariableFeatures(obj, assay = 'RNA')
obj <- ScaleData(obj, assay = 'RNA')

# Normalize ADT (CLR normalization)
obj <- NormalizeData(obj, assay = 'ADT', normalization.method = 'CLR', margin = 2)
obj <- ScaleData(obj, assay = 'ADT')

Weighted Nearest Neighbor (WNN) Clustering

Goal: Jointly cluster cells using both RNA and protein (or ATAC) modalities, weighting each modality's contribution per cell.

Approach: Run PCA separately on each modality, build a weighted nearest neighbor graph that adaptively combines both reductions, then cluster and embed on the combined WNN graph.

r
# Dimensionality reduction for each modality
obj <- RunPCA(obj, assay = 'RNA', reduction.name = 'pca')
obj <- RunPCA(obj, assay = 'ADT', reduction.name = 'apca',
              features = rownames(obj[['ADT']]))

# WNN graph combining both modalities
obj <- FindMultiModalNeighbors(obj,
    reduction.list = list('pca', 'apca'),
    dims.list = list(1:30, 1:18))

# Cluster on WNN graph
obj <- FindClusters(obj, graph.name = 'wsnn', resolution = 0.5)

# UMAP on WNN
obj <- RunUMAP(obj, nn.name = 'weighted.nn', reduction.name = 'wnn.umap')

Visualize

r
# UMAP colored by cluster
DimPlot(obj, reduction = 'wnn.umap', label = TRUE)

# ADT expression on UMAP
FeaturePlot(obj, features = c('adt_CD3', 'adt_CD19', 'adt_CD14'),
            reduction = 'wnn.umap')

# Compare modality weights
VlnPlot(obj, features = 'RNA.weight', group.by = 'seurat_clusters')

10X Multiome (RNA + ATAC)

Load Data

r
library(Seurat)
library(Signac)

# Read RNA counts
rna_counts <- Read10X_h5('filtered_feature_bc_matrix.h5')$`Gene Expression`

# Read ATAC fragments
atac_counts <- Read10X_h5('filtered_feature_bc_matrix.h5')$Peaks
fragments <- CreateFragmentObject('atac_fragments.tsv.gz')

# Create multiome object
obj <- CreateSeuratObject(counts = rna_counts, assay = 'RNA')
obj[['ATAC']] <- CreateChromatinAssay(counts = atac_counts, fragments = fragments,
                                       genome = 'hg38', min.cells = 5)

Process ATAC

r
# ATAC QC
obj <- NucleosomeSignal(obj)
obj <- TSSEnrichment(obj)

# ATAC normalization
obj <- RunTFIDF(obj, assay = 'ATAC')
obj <- FindTopFeatures(obj, assay = 'ATAC', min.cutoff = 'q0')
obj <- RunSVD(obj, assay = 'ATAC')

Joint Analysis

r
# RNA processing
DefaultAssay(obj) <- 'RNA'
obj <- NormalizeData(obj) %>% FindVariableFeatures() %>% ScaleData() %>% RunPCA()

# WNN integration
obj <- FindMultiModalNeighbors(obj, reduction.list = list('pca', 'lsi'),
                                dims.list = list(1:30, 2:30))
obj <- RunUMAP(obj, nn.name = 'weighted.nn', reduction.name = 'wnn.umap')
obj <- FindClusters(obj, graph.name = 'wsnn')

Scanpy/MuData (Python)

CITE-seq with MuData

python
import scanpy as sc
import muon as mu
from muon import prot as pt

# Load multimodal data
mdata = mu.read_10x_h5('filtered_feature_bc_matrix.h5')

# Access modalities
rna = mdata.mod['rna']
prot = mdata.mod['prot']

# Process RNA
sc.pp.filter_cells(rna, min_genes=200)
sc.pp.normalize_total(rna, target_sum=1e4)
sc.pp.log1p(rna)
sc.pp.highly_variable_genes(rna)
sc.tl.pca(rna)

# Process protein (CLR normalization)
pt.pp.clr(prot)

# Multi-omics factor analysis
mu.tl.mofa(mdata, n_factors=20)

# Joint UMAP
mu.tl.umap(mdata)
mu.pl.umap(mdata, color=['rna:leiden', 'prot:CD3'])

Integration Metrics

Modality Weights

r
# Check how much each modality contributes per cell
weights <- obj@reductions$wnn@misc$weights

# Average weight by cluster
aggregate(weights, by = list(obj$seurat_clusters), mean)

Correlation Between Modalities

python
import numpy as np

# Correlate RNA and protein for same genes/proteins
common = set(rna.var_names) & set(prot.var_names)
for gene in common:
    rna_expr = rna[:, gene].X.toarray().flatten()
    prot_expr = prot[:, gene].X.toarray().flatten()
    corr = np.corrcoef(rna_expr, prot_expr)[0, 1]
    print(f'{gene}: r={corr:.3f}')

Marker Discovery

Multi-Modal Markers

r
# Find markers using both modalities
DefaultAssay(obj) <- 'RNA'
rna_markers <- FindAllMarkers(obj, only.pos = TRUE)

DefaultAssay(obj) <- 'ADT'
adt_markers <- FindAllMarkers(obj, only.pos = TRUE)

# Combine
all_markers <- rbind(
    transform(rna_markers, modality = 'RNA'),
    transform(adt_markers, modality = 'ADT')
)

Related Skills

  • single-cell/data-io - Loading single-cell data
  • single-cell/clustering - Clustering methods
  • single-cell/markers-annotation - Cell type annotation
  • chip-seq/peak-calling - For ATAC peak calling

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

FreedomIntelligence/OpenClaw-Medical-Skills

vcf-annotator

Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

chemist-analyst

Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-alignment-io

Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

sleep-analyzer

分析睡眠数据、识别睡眠模式、评估睡眠质量，并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-hi-c-analysis-matrix-operations

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

2,009 275
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results